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discretize() converts a numeric vector into a factor with bins having approximately the same number of data points (based on a training set).


discretize(x, ...)

# S3 method for default
discretize(x, ...)

# S3 method for numeric
  cuts = 4,
  labels = NULL,
  prefix = "bin",
  keep_na = TRUE,
  infs = TRUE,
  min_unique = 10,

# S3 method for discretize
predict(object, new_data, ...)



A numeric vector


Options to pass to stats::quantile() that should not include x or probs.


An integer defining how many cuts to make of the data.


A character vector defining the factor levels that will be in the new factor (from smallest to largest). This should have length cuts+1 and should not include a level for missing (see keep_na below).


A single parameter value to be used as a prefix for the factor levels (e.g. bin1, bin2, ...). If the string is not a valid R name, it is coerced to one. If prefix = NULL then the factor levels will be labelled according to the output of cut().


A logical for whether a factor level should be created to identify missing values in x. If keep_na is set to TRUE then na.rm = TRUE is used when calling stats::quantile().


A logical indicating whether the smallest and largest cut point should be infinite.


An integer defining a sample size line of dignity for the binning. If (the number of unique values)/(cuts+1) is less than min_unique, no discretization takes place.


An object of class discretize.


A new numeric object to be binned.


discretize returns an object of class discretize and predict.discretize returns a factor vector.


discretize estimates the cut points from x using percentiles. For example, if cuts = 3, the function estimates the quartiles of x and uses these as the cut points. If cuts = 2, the bins are defined as being above or below the median of x.

The predict method can then be used to turn numeric vectors into factor vectors.

If keep_na = TRUE, a suffix of "_missing" is used as a factor level (see the examples below).

If infs = FALSE and a new value is greater than the largest value of x, a missing value will result.


data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

#> [1] 47.1
discretize(biomass_tr$carbon, cuts = 2)
#> Bins: 3 (includes missing category)
#> Breaks: -Inf, 47.1, Inf
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE)
#> Bins: 3 (includes missing category)
#> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
#> Bins: 2
#> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin")
#> Warning: The prefix "maybe a bad idea to bin" is not a valid R name. It has been
#> changed to "".
#> Bins: 3 (includes missing category)
#> Breaks: -Inf, 47.1, Inf

carbon_binned <- discretize(biomass_tr$carbon)
table(predict(carbon_binned, biomass_tr$carbon))
#> bin1 bin2 bin3 bin4 
#>  114  115  113  114 

carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE)
predict(carbon_no_infs, c(50, 100))
#> [1] bin4 <NA>
#> Levels: bin1 bin2 bin3 bin4